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an IA just recommended this sub
submitted by /u/instakill200
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After long search I've found a Gaze Redirecting AI that does NOT need a NVIDIA card. Can someone help me install it?
Heya there. A month or so ago I've read for the first time about the Gaze Redirecting AI technology provided by Nvidia, I think it's called Maxine ( or Maxine has to be the program through which you can achieve this ). However I have an AMD card so I coudn't run it.
I have found a github page of a young coder who, apparently, was able to achieve such a thing before Nvidia came out with its software.
However I haven't been able to install it yet because I'm not a coder and the instructions do not come crystal clear to me. It seems made for people who already know about these sort of programs.
Here is the page: https://github.com/chihfanhsu/gaze_correction
Please let me know if you manage to install it and how you did that. You might DM me aswell if you want!
submitted by /u/heldex
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Does anyone know how to put an A.I voice over an mp3 file so it sounds like the A.I is singing the song?
I was wondering how people make these videos, I wanted to make one myself because it would be really funny, but im not sure exactly how it works, does anyone know?
link for example: https://www.youtube.com/watch?v=li_OKCpPxM4
submitted by /u/Void_44
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( 43
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What if we add a "Thinker" block to a Encoder-Decoder language model?
submitted by /u/begmax
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( 42
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The A.I. Dilemma - March 9, 2023
submitted by /u/al-Assas
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( 42
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[D] WhatsApp group chat for sharing AI prompts and images - join us!
All are welcome :) just a bit of fun...
https://chat.whatsapp.com/BVqzerznn226l41xxi0oNC
submitted by /u/140BPMMaster
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( 43
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[P] Magic Copy - Meta's Segment Anything Model in a Chrome Extension
submitted by /u/kevmo314
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( 44
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[P] Datasynth: Synthetic data generation and normalization functions using LangChain + LLMs
We release Datasynth, a pipeline for synthetic data generation and normalization operations using LangChain and LLM APIs. Using Datasynth, you can generate absolutely synthetic datasets to train a task-specific model you can run on your own GPU.
For testing, we generated synthetic datasets for names, prices, and addresses then trained a Seq2Seq model for evaluation. Initial models for standardization are available on HuggingFace
Public code is available on GitHub
submitted by /u/tobiadefami
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( 44
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[P] A system for deep learning and reinforcement learning.
submitted by /u/NoteDancing
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[D] Are there no repercussions for breaking dual submission policies anymore? (ICLR & CVPR)
Looking through ICLR and CVPR papers, I came across a couple of papers that broke the dual submission policy and eventually got accepted in CVPR. With all the quiet talk about collusion rings and rigged reviews, does nobody care about the dual submission policy anymore?
Here is an example paper: [1] submitted to ICLR on Sep 22, withdrawn from ICLR on Nov 16 [2], but it was already submitted to CVPR on Nov 4 [3].
[1] Learning Rotation-Equivariant Features for Visual Correspondence - https://arxiv.org/abs/2303.15472
[2] https://openreview.net/forum?id=GCF6ZOA6Npk
[3] https://cvpr2023.thecvf.com/Conferences/2023/AcceptedPapers
submitted by /u/redlow0992
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( 45
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Principal component analysis for Gaussian process posteriors. (arXiv:2107.07115v2 [stat.ML] UPDATED)
This paper proposes an extension of principal component analysis for Gaussian
process (GP) posteriors, denoted by GP-PCA. Since GP-PCA estimates a
low-dimensional space of GP posteriors, it can be used for meta-learning, which
is a framework for improving the performance of target tasks by estimating a
structure of a set of tasks. The issue is how to define a structure of a set of
GPs with an infinite-dimensional parameter, such as coordinate system and a
divergence. In this study, we reduce the infiniteness of GP to the
finite-dimensional case under the information geometrical framework by
considering a space of GP posteriors that have the same prior. In addition, we
propose an approximation method of GP-PCA based on variational inference and
demonstrate the effectiveness of GP-PCA as meta-learning through experiments.
( 2
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Sequential Adversarial Anomaly Detection for One-Class Event Data. (arXiv:1910.09161v5 [stat.ML] UPDATED)
We consider the sequential anomaly detection problem in the one-class setting
when only the anomalous sequences are available and propose an adversarial
sequential detector by solving a minimax problem to find an optimal detector
against the worst-case sequences from a generator. The generator captures the
dependence in sequential events using the marked point process model. The
detector sequentially evaluates the likelihood of a test sequence and compares
it with a time-varying threshold, also learned from data through the minimax
problem. We demonstrate our proposed method's good performance using numerical
experiments on simulations and proprietary large-scale credit card fraud
datasets. The proposed method can generally apply to detecting anomalous
sequences.
( 2
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Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms. (arXiv:2304.03056v1 [math.PR])
In this work, we derive sharp non-asymptotic deviation bounds for weighted
sums of Dirichlet random variables. These bounds are based on a novel integral
representation of the density of a weighted Dirichlet sum. This representation
allows us to obtain a Gaussian-like approximation for the sum distribution
using geometry and complex analysis methods. Our results generalize similar
bounds for the Beta distribution obtained in the seminal paper Alfers and
Dinges [1984]. Additionally, our results can be considered a sharp
non-asymptotic version of the inverse of Sanov's theorem studied by Ganesh and
O'Connell [1999] in the Bayesian setting. Based on these results, we derive new
deviation bounds for the Dirichlet process posterior means with application to
Bayesian bootstrap. Finally, we apply our estimates to the analysis of the
Multinomial Thompson Sampling (TS) algorithm in multi-armed bandits and
significantly sharpen the existing regret bounds by making them independent of
the size of the arms distribution support.
( 2
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Static Fuzzy Bag-of-Words: a lightweight sentence embedding algorithm. (arXiv:2304.03098v1 [cs.CL])
The introduction of embedding techniques has pushed forward significantly the
Natural Language Processing field. Many of the proposed solutions have been
presented for word-level encoding; anyhow, in the last years, new mechanism to
treat information at an higher level of aggregation, like at sentence- and
document-level, have emerged. With this work we address specifically the
sentence embeddings problem, presenting the Static Fuzzy Bag-of-Word model. Our
model is a refinement of the Fuzzy Bag-of-Words approach, providing sentence
embeddings with a predefined dimension. SFBoW provides competitive performances
in Semantic Textual Similarity benchmarks, while requiring low computational
resources.
( 2
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Denoising diffusion probabilistic models for probabilistic energy forecasting. (arXiv:2212.02977v5 [cs.LG] UPDATED)
Scenario-based probabilistic forecasts have become vital for decision-makers
in handling intermittent renewable energies. This paper presents a recent
promising deep learning generative approach called denoising diffusion
probabilistic models. It is a class of latent variable models which have
recently demonstrated impressive results in the computer vision community.
However, to our knowledge, there has yet to be a demonstration that they can
generate high-quality samples of load, PV, or wind power time series, crucial
elements to face the new challenges in power systems applications. Thus, we
propose the first implementation of this model for energy forecasting using the
open data of the Global Energy Forecasting Competition 2014. The results
demonstrate this approach is competitive with other state-of-the-art deep
learning generative models, including generative adversarial networks,
variational autoencoders, and normalizing flows.
( 2
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HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions. (arXiv:2304.02811v1 [cs.LG])
Due to the complex behavior arising from non-uniqueness, symmetry, and
bifurcations in the solution space, solving inverse problems of nonlinear
differential equations (DEs) with multiple solutions is a challenging task. To
address this issue, we propose homotopy physics-informed neural networks
(HomPINNs), a novel framework that leverages homotopy continuation and neural
networks (NNs) to solve inverse problems. The proposed framework begins with
the use of a NN to simultaneously approximate known observations and conform to
the constraints of DEs. By utilizing the homotopy continuation method, the
approximation traces the observations to identify multiple solutions and solve
the inverse problem. The experiments involve testing the performance of the
proposed method on one-dimensional DEs and applying it to solve a
two-dimensional Gray-Scott simulation. Our findings demonstrate that the
proposed method is scalable and adaptable, providing an effective solution for
solving DEs with multiple solutions and unknown parameters. Moreover, it has
significant potential for various applications in scientific computing, such as
modeling complex systems and solving inverse problems in physics, chemistry,
biology, etc.
( 3
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Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification. (arXiv:2304.02836v1 [eess.IV])
The accuracy of predictive models for solitary pulmonary nodule (SPN)
diagnosis can be greatly increased by incorporating repeat imaging and medical
context, such as electronic health records (EHRs). However, clinically routine
modalities such as imaging and diagnostic codes can be asynchronous and
irregularly sampled over different time scales which are obstacles to
longitudinal multimodal learning. In this work, we propose a transformer-based
multimodal strategy to integrate repeat imaging with longitudinal clinical
signatures from routinely collected EHRs for SPN classification. We perform
unsupervised disentanglement of latent clinical signatures and leverage
time-distance scaled self-attention to jointly learn from clinical signatures
expressions and chest computed tomography (CT) scans. Our classifier is
pretrained on 2,668 scans from a public dataset and 1,149 subjects with
longitudinal chest CTs, billing codes, medications, and laboratory tests from
EHRs of our home institution. Evaluation on 227 subjects with challenging SPNs
revealed a significant AUC improvement over a longitudinal multimodal baseline
(0.824 vs 0.752 AUC), as well as improvements over a single cross-section
multimodal scenario (0.809 AUC) and a longitudinal imaging-only scenario (0.741
AUC). This work demonstrates significant advantages with a novel approach for
co-learning longitudinal imaging and non-imaging phenotypes with transformers.
( 3
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Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets. (arXiv:2304.02847v1 [cs.CV])
Deep networks have achieved impressive results on a range of well-curated
benchmark datasets. Surprisingly, their performance remains sensitive to
perturbations that have little effect on human performance. In this work, we
propose a novel extension of Mixup called Robustmix that regularizes networks
to classify based on lower-frequency spatial features. We show that this type
of regularization improves robustness on a range of benchmarks such as
Imagenet-C and Stylized Imagenet. It adds little computational overhead and,
furthermore, does not require a priori knowledge of a large set of image
transformations. We find that this approach further complements recent advances
in model architecture and data augmentation, attaining a state-of-the-art mCE
of 44.8 with an EfficientNet-B8 model and RandAugment, which is a reduction of
16 mCE compared to the baseline.
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Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound. (arXiv:2304.02724v1 [cs.CV])
Self-supervised pretraining has been observed to improve performance in
supervised learning tasks in medical imaging. This study investigates the
utility of self-supervised pretraining prior to conducting supervised
fine-tuning for the downstream task of lung sliding classification in M-mode
lung ultrasound images. We propose a novel pairwise relationship that couples
M-mode images constructed from the same B-mode image and investigate the
utility of data augmentation procedure specific to M-mode lung ultrasound. The
results indicate that self-supervised pretraining yields better performance
than full supervision, most notably for feature extractors not initialized with
ImageNet-pretrained weights. Moreover, we observe that including a vast volume
of unlabelled data results in improved performance on external validation
datasets, underscoring the value of self-supervision for improving
generalizability in automatic ultrasound interpretation. To the authors' best
knowledge, this study is the first to characterize the influence of
self-supervised pretraining for M-mode ultrasound.
( 2
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Application of Transformers based methods in Electronic Medical Records: A Systematic Literature Review. (arXiv:2304.02768v1 [cs.CL])
The combined growth of available data and their unstructured nature has
received increased interest in natural language processing (NLP) techniques to
make value of these data assets since this format is not suitable for
statistical analysis. This work presents a systematic literature review of
state-of-the-art advances using transformer-based methods on electronic medical
records (EMRs) in different NLP tasks. To the best of our knowledge, this work
is unique in providing a comprehensive review of research on transformer-based
methods for NLP applied to the EMR field. In the initial query, 99 articles
were selected from three public databases and filtered into 65 articles for
detailed analysis. The papers were analyzed with respect to the business
problem, NLP task, models and techniques, availability of datasets,
reproducibility of modeling, language, and exchange format. The paper presents
some limitations of current research and some recommendations for further
research.
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SoK: Machine Learning for Continuous Integration. (arXiv:2304.02829v1 [cs.SE])
Continuous Integration (CI) has become a well-established software
development practice for automatically and continuously integrating code
changes during software development. An increasing number of Machine Learning
(ML) based approaches for automation of CI phases are being reported in the
literature. It is timely and relevant to provide a Systemization of Knowledge
(SoK) of ML-based approaches for CI phases. This paper reports an SoK of
different aspects of the use of ML for CI. Our systematic analysis also
highlights the deficiencies of the existing ML-based solutions that can be
improved for advancing the state-of-the-art.
( 2
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Hybrid Zonotopes Exactly Represent ReLU Neural Networks. (arXiv:2304.02755v1 [cs.LG])
We show that hybrid zonotopes offer an equivalent representation of
feed-forward fully connected neural networks with ReLU activation functions.
Our approach demonstrates that the complexity of binary variables is equal to
the total number of neurons in the network and hence grows linearly in the size
of the network. We demonstrate the utility of the hybrid zonotope formulation
through three case studies including nonlinear function approximation, MPC
closed-loop reachability and verification, and robustness of classification on
the MNIST dataset.
( 2
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Graph Representation Learning for Interactive Biomolecule Systems. (arXiv:2304.02656v1 [q-bio.QM])
Advances in deep learning models have revolutionized the study of biomolecule
systems and their mechanisms. Graph representation learning, in particular, is
important for accurately capturing the geometric information of biomolecules at
different levels. This paper presents a comprehensive review of the
methodologies used to represent biological molecules and systems as
computer-recognizable objects, such as sequences, graphs, and surfaces.
Moreover, it examines how geometric deep learning models, with an emphasis on
graph-based techniques, can analyze biomolecule data to enable drug discovery,
protein characterization, and biological system analysis. The study concludes
with an overview of the current state of the field, highlighting the challenges
that exist and the potential future research directions.
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Logistic-Normal Likelihoods for Heteroscedastic Label Noise in Classification. (arXiv:2304.02849v1 [cs.LG])
A natural way of estimating heteroscedastic label noise in regression is to
model the observed (potentially noisy) target as a sample from a normal
distribution, whose parameters can be learned by minimizing the negative
log-likelihood. This loss has desirable loss attenuation properties, as it can
reduce the contribution of high-error examples. Intuitively, this behavior can
improve robustness against label noise by reducing overfitting. We propose an
extension of this simple and probabilistic approach to classification that has
the same desirable loss attenuation properties. We evaluate the effectiveness
of the method by measuring its robustness against label noise in
classification. We perform enlightening experiments exploring the inner
workings of the method, including sensitivity to hyperparameters, ablation
studies, and more.
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A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation. (arXiv:2304.02858v1 [cs.LG])
Class imbalance (CI) in classification problems arises when the number of
observations belonging to one class is lower than the other classes. Ensemble
learning that combines multiple models to obtain a robust model has been
prominently used with data augmentation methods to address class imbalance
problems. In the last decade, a number of strategies have been added to enhance
ensemble learning and data augmentation methods, along with new methods such as
generative adversarial networks (GANs). A combination of these has been applied
in many studies, but the true rank of different combinations would require a
computational review. In this paper, we present a computational review to
evaluate data augmentation and ensemble learning methods used to address
prominent benchmark CI problems. We propose a general framework that evaluates
10 data augmentation and 10 ensemble learning methods for CI problems. Our
objective was to identify the most effective combination for improving
classification performance on imbalanced datasets. The results indicate that
combinations of data augmentation methods with ensemble learning can
significantly improve classification performance on imbalanced datasets. These
findings have important implications for the development of more effective
approaches for handling imbalanced datasets in machine learning applications.
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Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systems. (arXiv:2304.02947v1 [cs.LG])
This paper presents the Real-time Adaptive and Interpretable Detection (RAID)
algorithm. The novel approach addresses the limitations of state-of-the-art
anomaly detection methods for multivariate dynamic processes, which are
restricted to detecting anomalies within the scope of the model training
conditions. The RAID algorithm adapts to non-stationary effects such as data
drift and change points that may not be accounted for during model development,
resulting in prolonged service life. A dynamic model based on joint probability
distribution handles anomalous behavior detection in a system and the root
cause isolation based on adaptive process limits. RAID algorithm does not
require changes to existing process automation infrastructures, making it
highly deployable across different domains. Two case studies involving real
dynamic system data demonstrate the benefits of the RAID algorithm, including
change point adaptation, root cause isolation, and improved detection accuracy.
( 2
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